%!TEX root = Thesis.tex 

\chapter{Introduction}
\section{Problem formulation}

When we consume energy the information about our usage is collected by various meters. This information is usually obtained once a month or year for billing purposes. Some energy providers have started to collect information about our energy consumption through 'smart meters'. The smart meters measure consumption over a much shorter time-interval (per hour, even minutes sometimes), and can usually pinpoint the devices that have contributed at a certain time. This private energy consumption data is currently stored on utilities servers, outside of our control. 

The advantage of using smart-meters is the increase in service that can be provided to the consumer. A simple service such as visualization of consumer data has already been proven popular and reduced energy consumption in many cases. Some providers of this service even made energy reduction into competitive games with great success. Other services could include information of when energy-prices are low, detection of unauthorized use of electricity, control of the maximum amount of energy that can be consumed at any time, targeted suggestions on how to save energy etc. 

External entities such as Pachube, Facebook and Greenbutton are all providing some, or all, of the services mentioned. They either function in cooperation with the meters already installed in the users home, or encourage users to install more meters on their own account. People provide their data voluntarily to the external entity who can give them the exact service they want. 

There is thus a growing need to investigate the tradeoff between the services we want and the privacy we need. What patterns can be recognized and what can we detect about people from these data? Can we for instance detect wether or not people are home, who is home or what they are doing? Would it be possible to even classify a home in a certain category and predict who they are voting for, or how much money they make? 

This thesis will investigate the potential threats of not retaining control of our private energy consumption data.

To be handed in:
- An analysis of what data is available currently, what the usage policies are of the different stakehoders and how they are enforced.
- An analysis and comparison of different detection and classification methods for time-series of data
- A design and evaluation of a detection and a classification model based on pachube/utility data
- A reflection of what could be done to avoid outsiders to detect or classify the data

Method:
- studying various Machine Learning and Data Mining methods to handle time series data and build models of the world
- acquiring data from pachube (A site where people upload their data themselves) and potentially other utilities.
- Creating detection and classification models learnt from data, without a ground truth to compare the models with


